Training and testing binding thrombin dataset

@@ -111,8 +111,10 @@ if __name__ == "__main__":
         joblib.dump(X_train, os.path.join(args.outputModelPath, args.inputTrainingData + '.jlb'))
         joblib.dump(y_train, os.path.join(args.outputModelPath, args.inputTrainingData + '.class.jlb'))
     else:
+        print("   Saving matrix and classes...")
         X_train = joblib.load(os.path.join(args.outputModelPath, args.inputTrainingData + '.jlb'))
         y_train = joblib.load(os.path.join(args.outputModelPath, args.inputTrainingData + '.class.jlb'))
+        print("      Done!")
 
     print("   Number of training classes: {}".format(len(y_train)))
     print("   Number of training class A: {}".format(y_train.count('A')))
@@ -139,20 +141,25 @@ if __name__ == "__main__":
         joblib.dump(X_test, os.path.join(args.outputModelPath, args.inputTestingData + '.jlb'))
         joblib.dump(y_test, os.path.join(args.outputModelPath, args.inputTestingClasses + '.class.jlb'))
     else:
+        print("   Saving matrix and classes...")
         X_test = joblib.load(os.path.join(args.outputModelPath, args.inputTestingData + '.jlb'))
         y_test = joblib.load(os.path.join(args.outputModelPath, args.inputTestingClasses + '.class.jlb'))
+        print("      Done!")
 
     print("   Number of testing classes: {}".format(len(y_test)))
     print("   Number of testing class A: {}".format(y_test.count('A')))
     print("   Number of testing class I: {}".format(y_test.count('I')))
     print("   Shape of testing matrix: {}".format(X_test.shape))
 
-    if args.classifier == "MultinomialNB":
+    if args.classifier == "BernoulliNB":
         classifier = BernoulliNB()
     elif args.classifier == "SVM":
         classifier = SVC()
     elif args.classifier == "NearestCentroid":
         classifier = NearestCentroid()
+    else:
+        print("Bad classifier")
+        exit()
 
     print("Training...")
     classifier.fit(X_train, y_train)